| Context and information awareness is generally driven by the choice of available data acquisition mechanisms. In the design of context and situation aware systems, sensors act as the primary means for data acquisition. Therefore, to make such systems accurate and effective sensors need to provide data that is comprehensive, and reflects real time situations and events with high accuracy. Such data is not always readily available, and it may require a considerable number of in-field experiments with prototypes to gather it. In the absence of good sensor data, the situation aware models and functionality become largely ineffective. This paper proposes the use of qualitative and quantitative information gathered from user research to directly generate artificial sensor data. It is our belief that data obtained in this manner is more accurate in terms of user/environmental requirements and preferences, and is comprehensive enough to include commonly occurring as well as rarely occurring situations and events. This approach is similar to end-user tailoring, and preference elicitation methodologies. Furthermore, this approach of sensor-less data acquisition allows designers to generate models that can be evaluated first before narrowing down upon the selection of sensors, sensor attributes, and other implementation infrastructure keeping the design cost low. We present this approach with the help of a case study involving the design of interruption aware cell phone simulations. Here we illustrate how this approach was used, and also present results which indicate that users are generally satisfied with behavior of the design. |